Artem KirsanovThis video is my take on 3B1B's Summer of Math Exposition (SoME) competition
It explains in pretty intuitive terms how ideas from topology (or "rubber geometry") can be used in neuroscience, to help us understand the way information is embedded in high-dimensional representations inside neural circuits
OUTLINE: 00:00 Introduction 01:34 - Brief neuroscience background 06:23 - Topology and the notion of a manifold 11:48 - Dimension of a manifold 15:06 - Number of holes (genus) 18:49 - Putting it all together
Special thanks to Crimson Ghoul for providing English subtitles! ____________ Main paper: Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A. & Fiete, I. The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nat Neurosci 22, 1512–1520 (2019).
_________________________ Other relevant references: 1.Jazayeri, M. & Ostojic, S. Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity. arXiv:2107.04084 [q-bio] (2021). 2.Gallego, J. A., Perich, M. G., Chowdhury, R. H., Solla, S. A. & Miller, L. E. Long-term stability of cortical population dynamics underlying consistent behavior. Nat Neurosci 23, 260–270 (2020). 3.Bernardi, S. et al. The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex. Cell 183, 954-967.e21 (2020). 4.Shine, J. M. et al. Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nat Neurosci 22, 289–296 (2019). 5.Remington, E. D., Narain, D., Hosseini, E. A. & Jazayeri, M. Flexible Sensorimotor Computations through Rapid Reconfiguration of Cortical Dynamics. Neuron 98, 1005-1019.e5 (2018). 6.Low, R. J., Lewallen, S., Aronov, D., Nevers, R. & Tank, D. W. Probing variability in a cognitive map using manifold inference from neural dynamics. http://biorxiv.org/lookup/doi/10.1101/418939 (2018) doi:10.1101/418939. 7.Elsayed, G. F., Lara, A. H., Kaufman, M. T., Churchland, M. M. & Cunningham, J. P. Reorganization between preparatory and movement population responses in motor cortex. Nat Commun 7, 13239 (2016). 8.Peyrache, A., Lacroix, M. M., Petersen, P. C. & Buzsáki, G. Internally organized mechanisms of the head direction sense. Nat Neurosci 18, 569–575 (2015). 9.Dabaghian, Y., Mémoli, F., Frank, L. & Carlsson, G. A Topological Paradigm for Hippocampal Spatial Map Formation Using Persistent Homology. PLoS Comput Biol 8, e1002581 (2012). 10.Yu, B. M. et al. Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity. Journal of Neurophysiology 102, 614–635 (2009). 11.Singh, G. et al. Topological analysis of population activity in visual cortex. Journal of Vision 8, 11–11 (2008).
Voltage traces and spike trains were obtained from atrificially simulated neurons using BRIAN2 python package: Stimberg, M, Brette, R, Goodman, DFM. “Brian 2, an Intuitive and Efficient Neural Simulator.” eLife 8 (2019): e47314. doi: 10.7554/eLife.47314.
Generated spike trains were later analysed to obtain rate curves with the help of ELEPHANT: Denker M, Yegenoglu A, Grün S (2018) Collaborative HPC-enabled workflows on the HBP Collaboratory using the Elephant framework. Neuroinformatics 2018, P19. doi: 10.12751/incf.ni2018.0019
Neural manifolds - The Geometry of BehaviourArtem Kirsanov2021-08-22 | This video is my take on 3B1B's Summer of Math Exposition (SoME) competition
It explains in pretty intuitive terms how ideas from topology (or "rubber geometry") can be used in neuroscience, to help us understand the way information is embedded in high-dimensional representations inside neural circuits
OUTLINE: 00:00 Introduction 01:34 - Brief neuroscience background 06:23 - Topology and the notion of a manifold 11:48 - Dimension of a manifold 15:06 - Number of holes (genus) 18:49 - Putting it all together
Special thanks to Crimson Ghoul for providing English subtitles! ____________ Main paper: Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A. & Fiete, I. The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nat Neurosci 22, 1512–1520 (2019).
_________________________ Other relevant references: 1.Jazayeri, M. & Ostojic, S. Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity. arXiv:2107.04084 [q-bio] (2021). 2.Gallego, J. A., Perich, M. G., Chowdhury, R. H., Solla, S. A. & Miller, L. E. Long-term stability of cortical population dynamics underlying consistent behavior. Nat Neurosci 23, 260–270 (2020). 3.Bernardi, S. et al. The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex. Cell 183, 954-967.e21 (2020). 4.Shine, J. M. et al. Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nat Neurosci 22, 289–296 (2019). 5.Remington, E. D., Narain, D., Hosseini, E. A. & Jazayeri, M. Flexible Sensorimotor Computations through Rapid Reconfiguration of Cortical Dynamics. Neuron 98, 1005-1019.e5 (2018). 6.Low, R. J., Lewallen, S., Aronov, D., Nevers, R. & Tank, D. W. Probing variability in a cognitive map using manifold inference from neural dynamics. http://biorxiv.org/lookup/doi/10.1101/418939 (2018) doi:10.1101/418939. 7.Elsayed, G. F., Lara, A. H., Kaufman, M. T., Churchland, M. M. & Cunningham, J. P. Reorganization between preparatory and movement population responses in motor cortex. Nat Commun 7, 13239 (2016). 8.Peyrache, A., Lacroix, M. M., Petersen, P. C. & Buzsáki, G. Internally organized mechanisms of the head direction sense. Nat Neurosci 18, 569–575 (2015). 9.Dabaghian, Y., Mémoli, F., Frank, L. & Carlsson, G. A Topological Paradigm for Hippocampal Spatial Map Formation Using Persistent Homology. PLoS Comput Biol 8, e1002581 (2012). 10.Yu, B. M. et al. Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity. Journal of Neurophysiology 102, 614–635 (2009). 11.Singh, G. et al. Topological analysis of population activity in visual cortex. Journal of Vision 8, 11–11 (2008).
Voltage traces and spike trains were obtained from atrificially simulated neurons using BRIAN2 python package: Stimberg, M, Brette, R, Goodman, DFM. “Brian 2, an Intuitive and Efficient Neural Simulator.” eLife 8 (2019): e47314. doi: 10.7554/eLife.47314.
Generated spike trains were later analysed to obtain rate curves with the help of ELEPHANT: Denker M, Yegenoglu A, Grün S (2018) Collaborative HPC-enabled workflows on the HBP Collaboratory using the Elephant framework. Neuroinformatics 2018, P19. doi: 10.12751/incf.ni2018.0019
#SoME1Differential Equations: The Language of ChangeArtem Kirsanov2024-10-08 | To try everything Brilliant has to offer—free—for a full 30 days, visit brilliant.org/ArtemKirsanov . You’ll also get 20% off an annual premium subscription
My name is Artem, I'm a graduate student at NYU Center for Neural Science and researcher at Flatiron Institute (Center for Computational Neuroscience).
In this video, we explore the fascinating world of dynamical systems and differential equations, powerful tools for understanding how things change over time. We introduce key concepts like state variables, phase portraits, and limit cycles, using intuitive examples such as predator-prey models to visualize complex mathematical ideas.
This video was sponsored by BrilliantThe Key Equation Behind ProbabilityArtem Kirsanov2024-08-23 | Get 4 months extra on a 2 year plan here: nordvpn.com/artemkirsanov. It’s risk free with Nord’s 30 day money-back guarantee!
My name is Artem, I'm a graduate student at NYU Center for Neural Science and researcher at Flatiron Institute (Center for Computational Neuroscience).
In this video, we explore the fundamental concepts that underlie probability theory and its applications in neuroscience and machine learning. We begin with the intuitive idea of surprise and its relation to probability, using real-world examples to illustrate these concepts. From there, we move into more advanced topics: 1) Entropy – measuring the average surprise in a probability distribution. 2) Cross-entropy and the loss of information when approximating one distribution with another. 3) Kullback-Leibler (KL) divergence and its role in quantifying the difference between two probability distributions.
OUTLINE: 00:00 Introduction 02:00 Sponsor: NordVPN 04:07 What is probability (Bayesian vs Frequentist) 06:42 Probability Distributions 10:17 Entropy as average surprisal 13:53 Cross-Entropy and Internal models 19:20 Kullback–Leibler (KL) divergence 20:46 Objective functions and Cross-Entropy minimization 24:22 Conclusion & Outro
CREDITS: Special thanks to Crimson Ghoul for providing English subtitles!
In this video we explore Boltzmann Machines – one of the first generative models that learns probability distribution of data, leveraging stochastic rules and latent representations.
Socials: X/Twitter: https://x.com/ArtemKRSV Patreon (to view the extended script): patreon.com/artemkirsanov
OUTLINE: 00:00 Introduction 01:56 Goal of Boltzmann Machines 05:26 Boltzmann Distribution 13:29 Stochastic Update Rule 17:39 Contrastive Hebbian Rule 25:41 Hidden Units 28:25 Restricted Boltzmann Machines 29:38 Conclusion & Outro
References: 1. Ackley, D., Hinton, G. & Sejnowski, T. A learning algorithm for boltzmann machines. Cognitive Science 9, 147–169 (1985). 2. Downing, K. L. Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks. (The MIT Press, Cambridge, Massachusetts, 2023). 3. Hinton, G. E. & Salakhutdinov, R. R. Reducing the Dimensionality of Data with Neural Networks. Science 313, 504–507 (2006). 4. Hinton, G. E. A Practical Guide to Training Restricted Boltzmann Machines. in Neural Networks: Tricks of the Trade (eds. Montavon, G., Orr, G. B. & Müller, K.-R.) vol. 7700 599–619 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2012).
Special thanks to Crimson Ghoul for providing English subtitles!A Brain-Inspired Algorithm For MemoryArtem Kirsanov2024-07-03 | Get 20% off at shortform.com/artem
In this video we will explore the concept of Hopfield networks – a foundational model of associative memory that underlies many important ideas in neuroscience and machine learning, such as Boltzmann machines and Dense associative memory.
In this video we will talk about backpropagation – an algorithm powering the entire field of machine learning and try to derive it from first principles.
OUTLINE: 00:00 Introduction 01:28 Historical background 02:50 Curve Fitting problem 06:26 Random vs guided adjustments 09:43 Derivatives 14:34 Gradient Descent 16:23 Higher dimensions 21:36 Chain Rule Intuition 27:01 Computational Graph and Autodiff 36:24 Summary 38:16 Shortform 39:20 Outro
Jürgen Schmidhuber's blog on the history of backprop: https://people.idsia.ch/~juergen/who-invented-backpropagation.html
CREDITS: Icons by freepik.comMy Strategy To Consume Information EffectivelyArtem Kirsanov2023-09-07 | For a FREE trial and 20% discount to Shortform go to shortform.com/artem And to download the Shortform AI browser extension, visit bit.ly/45DCpuM
My name is Artem, I'm a neuroscience PhD student at NYU. In this video I talk about how I use summaries to consume knowledge from books and video lectures.
OUTLINE: 00:00 Introduction 01:39 Why summaries are useful 04:43 Shortform overview 07:58 My workflow with Obsidian 11:52 AI summaries of YouTube videos 16:40 LimitationsHow I make science animationsArtem Kirsanov2023-08-07 | To try everything Brilliant has to offer—free—for a full 30 days, visit brilliant.org/ArtemKirsanov The first 200 of you will get 20% off Brilliant’s annual premium subscription.
My name is Artem, I'm a neuroscience PhD student at NYU. In this long-requested video I share the creative process behind my videos – what software I use and break down some of my previous animations.
--- This video was sponsored by BrilliantBuilding Blocks of Memory in the BrainArtem Kirsanov2023-07-06 | To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov The first 200 of you will get 20% off Brilliant’s annual premium subscription.
My name is Artem, I'm a computational neuroscience student and researcher. In this video we discuss engrams – fundamental units of memory in the brain. We explore what engrams are, how memory is allocated, where it is stored, and how different memories become linked with each other.
OUTLINE: 00:00 - Introduction 00:39 - Historical background 01:44 - Fear conditioning paradigm 03:38 - Immediate-early genes as memory markers 08:13 - Engrams are necessary and sufficient for recall 10:16 - Excitabiliy and memory allocation 16:19 - Brain-wide engrams 18:12 - Linking memories together 24:20 - Summary 25:33 - Brilliant 27:09 - Outro
Special thanks to Crimson Ghoul for providing English subtitles
REFERENCES (in no particular order): 1. Robins, S. The 21st century engram. WIRES Cognitive Science e1653 (2023) doi:10.1002/wcs.1653. 2. Roy, D. S. et al. Brain-wide mapping reveals that engrams for a single memory are distributed across multiple brain regions. Nat Commun 13, 1799 (2022). 3. Josselyn, S. A. & Tonegawa, S. Memory engrams: Recalling the past and imagining the future. Science 367, eaaw4325 (2020). 4. Chen, L. et al. The role of intrinsic excitability in the evolution of memory: Significance in memory allocation, consolidation, and updating. Neurobiology of Learning and Memory 173, 107266 (2020). 5. Rao-Ruiz, P., Yu, J., Yu, J. J., Kushner, S. A. & Josselyn, S. A. Neuronal competition: microcircuit mechanisms define the sparsity of the engram. Current Opinion in Neurobiology 54, 163–170 (2019). 6. Josselyn, S. A. & Frankland, P. W. Memory Allocation: Mechanisms and Function. Annu. Rev. Neurosci. 41, 389–413 (2018). 7. Choi, J.-H. et al. Interregional synaptic maps among engram cells underlie memory formation. Science 360, 430–435 (2018). 8. Abdou, K. et al. Synapse-specific representation of the identity of overlapping memory engrams. Science 360, 1227–1231 (2018). 9. Yokose, J. et al. Overlapping memory trace indispensable for linking, but not recalling, individual memories. Science 355, 398–403 (2017). 10. Rashid, A. J. et al. Competition between engrams influences fear memory formation and recall. Science 353, 383–387 (2016). 11. Poo, M. et al. What is memory? The present state of the engram. BMC Biol 14, 40 (2016). 12. Park, S. et al. Neuronal Allocation to a Hippocampal Engram. Neuropsychopharmacol 41, 2987–2993 (2016). 13. Morrison, D. J. et al. Parvalbumin interneurons constrain the size of the lateral amygdala engram. Neurobiology of Learning and Memory 135, 91–99 (2016). 14. Minatohara, K., Akiyoshi, M. & Okuno, H. Role of Immediate-Early Genes in Synaptic Plasticity and Neuronal Ensembles Underlying the Memory Trace. Front. Mol. Neurosci. 8, (2016). 15. Josselyn, S. A., Köhler, S. & Frankland, P. W. Finding the engram. Nat Rev Neurosci 16, 521–534 (2015). 16. Yiu, A. P. et al. Neurons Are Recruited to a Memory Trace Based on Relative Neuronal Excitability Immediately before Training. Neuron 83, 722–735 (2014). 17. Redondo, R. L. et al. Bidirectional switch of the valence associated with a hippocampal contextual memory engram. Nature 513, 426–430 (2014). 18. Ramirez, S. et al. Creating a False Memory in the Hippocampus. Science 341, 387–391 (2013). 19. Liu, X. et al. Optogenetic stimulation of a hippocampal engram activates fear memory recall. Nature 484, 381–385 (2012). 20. Silva, A. J., Zhou, Y., Rogerson, T., Shobe, J. & Balaji, J. Molecular and Cellular Approaches to Memory Allocation in Neural Circuits. Science 326, 391–395 (2009).
This video was sponsored by BrilliantCan We Build an Artificial Hippocampus?Artem Kirsanov2023-04-30 | To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov The first 200 of you will get 20% off Brilliant’s annual premium subscription.
My name is Artem, I'm a computational neuroscience student and researcher. In this video we discuss the Tolman-Eichenbaum Machine – a computational model of a hippocampal formation, which unifies memory and spatial navigation under a common framework.
OUTLINE: 00:00 - Introduction 01:13 - Motivation: Agents, Rewards and Actions 03:17 - Prediction Problem 05:58 - Model architecture 06:46 - Position module 07:40 - Memory module 08:57 - Running TEM step-by-step 11:37 - Model performance 13:33 - Cellular representations 17:48 - TEM predicts remapping laws 19:37 - Recap and Acknowledgments 20:53 - TEM as a Transformer network 21:55 - Brilliant 23:19 - Outro
REFERENCES: 1. Whittington, J. C. R. et al. The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation. Cell 183, 1249-1263.e23 (2020). 2. Whittington, J. C. R., Warren, J. & Behrens, T. E. J. Relating transformers to models and neural representations of the hippocampal formation. Preprint at http://arxiv.org/abs/2112.04035 (2022). 3. Whittington, J. C. R., McCaffary, D., Bakermans, J. J. W. & Behrens, T. E. J. How to build a cognitive map. Nat Neurosci 25, 1257–1272 (2022).
CREDITS: Special thanks to Crimson Ghoul for providing English subtitles!
This video was sponsored by BrilliantHow Your Brain Organizes InformationArtem Kirsanov2023-03-31 | To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov The first 200 of you will get 20% off Brilliant’s annual premium subscription.
My name is Artem, I'm a computational neuroscience student and researcher. In this video we talk about cognitive maps – internal models of outside world that the brain to generate flexible behavior that is generalized across contexts.
OUTLINE: 00:00 - Introduction 02:08 - Edward Tolman 03:48 - Zoo of neurons in hippocampal formation 06:40 - Non spatial mapping 08:21 - Graph formalism 12:21 - Latent spaces 17:22 - Factorized representations 21:51 - Summary 24:47 - Brilliant 26:19 - Outro
REFERENCES (in no particular order): 1. Behrens, T. E. J. et al. What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior. Neuron 100, 490–509 (2018). 2. Constantinescu, A. O., O’Reilly, J. X. & Behrens, T. E. J. Organizing conceptual knowledge in humans with a gridlike code. Science 352, 1464–1468 (2016). 3. Aronov, D., Nevers, R. & Tank, D. W. Mapping of a non-spatial dimension by the hippocampal–entorhinal circuit. Nature 543, 719–722 (2017). 4. Whittington, J. C. R., McCaffary, D., Bakermans, J. J. W. & Behrens, T. E. J. How to build a cognitive map. Nat Neurosci 25, 1257–1272 (2022). 5. Whittington, J., Muller, T., Mark, S., Barry, C. & Behrens, T. Generalisation of structural knowledge in the hippocampal-entorhinal system.
CREDITS: Special thanks to Crimson Ghoul for providing English subtitles!
This video was sponsored by BrilliantBrain Criticality - Optimizing Neural ComputationsArtem Kirsanov2023-03-05 | To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov/. The first 200 of you will get 20% off Brilliant’s annual premium subscription.
My name is Artem, I'm a computational neuroscience student and researcher. In this video we talk about the concept of critical point – how the brain might optimize information processing by hovering near a phase transition.
Special thanks to Crimson Ghoul for providing English subtitles!
OUTLINE: 00:00 Introduction 01:11 - Phase transitions in nature 05:05 - The Ising Model 09:33 - Correlation length and long-range communication 13:14 - Scale-free properties and power laws 20:20 - Neuronal avalanches 25:00 - The branching model 31:05 - Optimizing information transmission 34:06 - Brilliant.org 35:41 - Recap and outro
The book: https://mitpress.mit.edu/9780262544030/the-cortex-and-the-critical-point/
REFERENCES (in no particular order): 1. Zimmern, V. Why Brain Criticality Is Clinically Relevant: A Scoping Review. Front. Neural Circuits 14, 54 (2020). 2. Beggs, J. M. The criticality hypothesis: how local cortical networks might optimize information processing. Phil. Trans. R. Soc. A. 366, 329–343 (2008). 3. Beggs, J. M. The cortex and the critical point: understanding the power of emergence. (The MIT Press, 2022). 4. Heffern, E. F. W., Huelskamp, H., Bahar, S. & Inglis, R. F. Phase transitions in biology: from bird flocks to population dynamics. Proc. R. Soc. B. 288, 20211111 (2021). 5. Beggs, J. M. & Plenz, D. Neuronal Avalanches in Neocortical Circuits. J. Neurosci. 23, 11167–11177 (2003). 6. Avramiea, A.-E., Masood, A., Mansvelder, H. D. & Linkenkaer-Hansen, K. Long-Range Amplitude Coupling Is Optimized for Brain Networks That Function at Criticality. J. Neurosci. 42, 2221–2233 (2022). 7. O’Byrne, J. & Jerbi, K. How critical is brain criticality? Trends in Neurosciences 45, 820–837 (2022). 8. Haldeman, C. & Beggs, J. M. Critical Branching Captures Activity in Living Neural Networks and Maximizes the Number of Metastable States. Phys. Rev. Lett. 94, 058101 (2005). 9. Beggs, J. M. Being critical of criticality in the brain. Frontiers in Physiology.
CREDITS: Icons by biorender.com Brain 3D models were modeled with Blender software using publicly available BrainGlobe atlases (brainglobe.info/atlas-api)
This video was sponsored by BrilliantDendrites: Why Biological Neurons Are Deep Neural NetworksArtem Kirsanov2023-01-29 | Keep exploring at http://brilliant.org/ArtemKirsanov Get started for free, and hurry—the first 200 people get 20% off an annual premium subscription.
My name is Artem, I'm a computational neuroscience student and researcher. In this video we will see why individual neurons essentially function like deep convolutional neural networks, equipped with insane information processing capabilities as well as some of the physiological mechanisms, that account for such computational complexity.
OUTLINE: 00:00 Introduction 01:42 - Perceptrons 03:43 - Electrical excitability and action potential 07:12 - Cable theory: passive dendrites 09:03 - Active dendritic properties 12:10 - Human neurons as XOR gates 19:11 - Single neurons as deep neural networks 22:32 - Brilliant 23:57 - Recap and outro
Special thanks to Crimson Ghoul for providing English subtitles!
REFERENCES (in no particular order): 1. Bicknell, B. A., Bicknell, B. A. & Häusser, M. A synaptic learning rule for exploiting nonlinear dendritic computation. Neuron (2021) doi:10.1016/j.neuron.2021.09.044. 2. Matthew Larkum. Are dendrites conceptually useful? Neuroscience (2022) doi:10.1016/j.neuroscience.2022.03.008. 3. Polsky, A., Mel, B. W. & Schiller, J. Computational subunits in thin dendrites of pyramidal cells. Nature Neuroscience 7, 621–627 (2004). 4. Tran-Van-Minh, A. et al. Contribution of sublinear and supralinear dendritic integration to neuronal computations. Frontiers in Cellular Neuroscience 9, 67–67 (2015). 5. Gidon, A. et al. Dendritic action potentials and computation in human layer 2/3 cortical neurons. Science 367, 83–87 (2020). 6. London, M. & Häusser, M. DENDRITIC COMPUTATION. Annu. Rev. Neurosci. 28, 503–532 (2005). 7. Branco, T., Clark, B. A. & Häusser, M. Dendritic Discrimination of Temporal Input Sequences in Cortical Neurons. Science 329, 1671–1675 (2010). 8. Stuart, G. J. & Spruston, N. Dendritic integration: 60 years of progress. Nat Neurosci 18, 1713–1721 (2015). 9. Smith, S. L., Smith, I. T., Branco, T. & Häusser, M. Dendritic spikes enhance stimulus selectivity in cortical neurons in vivo. Nature 503, 115–120 (2013). 10. Beniaguev, D., Segev, I. & London, M. Single cortical neurons as deep artificial neural networks. Neuron 109, (2021). 11. Michalikova, M., Remme, M. W. H., Schmitz, D., Schreiber, S. & Kempter, R. Spikelets in pyramidal neurons: generating mechanisms, distinguishing properties, and functional implications. Reviews in the Neurosciences 31, 101–119 (2019). 12. Larkum, M. E., Wu, J., Duverdin, S. A. & Gidon, A. The Guide to Dendritic Spikes of the Mammalian Cortex In Vitro and In Vivo. Neuroscience 489, 15–33 (2022).
CREDITS: Icons by biorender.com Brain 3D models were modeled with Blender software using publicly available BrainGlobe atlases (brainglobe.info/atlas-api)
This video was sponsored by BrilliantA Map of Social Space in Your BrainArtem Kirsanov2022-12-25 | Shortform link: shortform.com/artem
My name is Artem, I'm a computational neuroscience student and researcher. In this video we talk about how hippocampus serves a "social map", representing information about conspecific individuals at different levels of abstraction.
OUTLINE: 00:00 Introduction 03:30 Overview of physical place cells 04:57 Social information in physical space 11:28 Abstract social space 15:36 Recap 16:12 Shortform 17:03 Outro
REFERENCES:
1. Omer, D. B., Maimon, S. R., Las, L. & Ulanovsky, N. Social place-cells in the bat hippocampus. Science 359, 218–224 (2018). 2. Tavares, R. M. et al. A Map for Social Navigation in the Human Brain. Neuron 87, 231–243 (2015). 3. Schafer, M. & Schiller, D. Navigating Social Space. Neuron 100, 476–489 (2018). 4. Eichenbaum, H. The Hippocampus as a Cognitive Map … of Social Space. Neuron 87, 9–11 (2015).
My name is Artem, I'm a computational neuroscience student and researcher. In this video we talk about theta rhythm - a rhythmic pattern of brain activity (4-12 Hz), which is essential for memory encoding and retrieval.
Special thanks to Crimson Ghoul for providing English subtitles!
REFERENCES (in no particular order): 1. Hummos A, Nair SS. An integrative model of the intrinsic hippocampal theta rhythm. Lytton WW, editor. PLoS ONE. 2017 Aug 7;12(8):e0182648. 2. Ferguson KA, Chatzikalymniou AP, Skinner FK. Combining theory, model and experiment to understand how theta rhythms are generated in the hippocampus 2017 3. Drieu C, Zugaro M. Hippocampal Sequences During Exploration: Mechanisms and Functions. Front Cell Neurosci. 2019 Jun 13;13:232. 4. Speers LJ, Cheyne KR, Cavani E, Hayward T, Schmidt R, Bilkey DK. Hippocampal Sequencing Mechanisms Are Disrupted in a Maternal Immune Activation Model of Schizophrenia Risk. J Neurosci. 2021 Aug 11;41(32):6954–65. 5. Foster DJ, Wilson MA. Hippocampal theta sequences. Hippocampus. 2007 Nov;17(11):1093–9. 6. Backus AR, Schoffelen JM, Szebényi S, Hanslmayr S, Doeller CF. Hippocampal-Prefrontal Theta Oscillations Support Memory Integration. Current Biology. 2016 Feb;26(4):450–7. 7. Pastalkova E, Itskov V, Amarasingham A, Buzsáki G. Internally Generated Cell Assembly Sequences in the Rat Hippocampus. Science. 2008 Sep 5;321(5894):1322–7. 8. Quiroga RQ, Reddy L, Kreiman G, Koch C, Fried I. Invariant visual representation by single neurons in the human brain. Nature. 2005 Jun;435(7045):1102–7. 9. Colgin LL. Mechanisms and Functions of Theta Rhythms. Annu Rev Neurosci. 2013 Jul 8;36(1):295–312. 10. Buzsáki G, Moser EI. Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nat Neurosci. 2013 Feb;16(2):130–8. 11. Buzsaki G. Rhythms of the Brain. 12. Colgin LL. Rhythms of the hippocampal network. Nat Rev Neurosci. 2016 Apr;17(4):239–49. 13. Goutagny R, Jackson J, Williams S. Self-generated theta oscillations in the hippocampus. Nat Neurosci. 2009 Dec;12(12):1491–3. 14. Buzsáki G, Tingley D. Space and Time: The Hippocampus as a Sequence Generator. Trends in Cognitive Sciences. 2018 Oct;22(10):853–69. 15. Buzsáki G. Theta Oscillations in the Hippocampus. Neuron. 2002 Jan;33(3):325–40. 16. Skaggs WE, McNaughton BL, Wilson MA, Barnes CA. Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences. Hippocampus. 1996;6(2):149–72. 17. Hasselmo ME, Stern CE. Theta rhythm and the encoding and retrieval of space and time. NeuroImage. 2014 Jan;85:656–66. 18. Wang Y, Romani S, Lustig B, Leonardo A, Pastalkova E. Theta sequences are essential for internally generated hippocampal firing fields. Nat Neurosci. 2015 Feb;18(2):282–8. 19. Reddy L, Self MW, Zoefel B, Poncet M, Possel JK, Peters JC, et al. Theta-phase dependent neuronal coding during sequence learning in human single neurons. Nat Commun. 2021 Dec;12(1):4839.
OUTLINE: 00:00 Introduction 00:48 Brain waves 01:18 Generation of theta rhythm 04:37 Functions of theta wave 08:13 Forming an integrated representation 11:05 Sequential organization 14:50 Phase precession 17:49 Conclusion 18:32 Sponsor message 19:36 Outro
CREDITS: Icons by biorender.com, flaticon.comWavelets: a mathematical microscopeArtem Kirsanov2022-08-15 | Wavelet transform is an invaluable tool in signal processing, which has applications in a variety of fields - from hydrodynamics to neuroscience. This revolutionary method allows us to uncover structures, which are present in the signal but are hidden behind the noise. The key feature of wavelet transform is that it performs function decomposition in both time and frequency domains.
In this video we will see how to build a wavelet toolkit step by step and discuss important implications and prerequisites along the way.
This is my entry for Summer of Math Exposition 2022 ( #SoME2 ). My name is Artem, I'm a computational neuroscience student and researcher at Moscow State University. Twitter: @artemkrsv
OUTLINE: 00:00 Introduction 01:55 Time and frequency domains 03:27 Fourier Transform 05:08 Limitations of Fourier 08:45 Wavelets - localized functions 10:34 Mathematical requirements for wavelets 12:17 Real Morlet wavelet 13:02 Wavelet transform overview 14:08 Mother wavelet modifications 15:46 Computing local similarity 18:08 Dot product of functions? 21:07 Convolution 24:55 Complex numbers 27:56 Wavelet scalogram 30:46 Uncertainty & Heisenberg boxes 33:16 Recap and conclusion
Credits: Special thanks to Crimson Ghoul for providing English subtitles!
Mathematical animations were done using manim (https://docs.manim.community/en/stable/) and matplotlib python libraries. 3D animations were done in BlenderSelf-study computational neuroscience | Coding, Textbooks, MathArtem Kirsanov2022-06-06 | Shortform link: shortform.com/artem
My name is Artem, I'm a computational neuroscience student and researcher. In this video I share my experience on getting started with computational neuroscience. We will talk about programming languages, learning to code, recommended textbooks and finding project ideas to practice with.
--------- OUTLINE: 0:00 Introduction 0:46 What is computational neuroscience 4:24 Necessary skills 5:42 Choosing programming language 6:50 Algorithmic thinking 8:36 Ways to practice coding 9:43 General neuroscience books 11:08 Computational neuroscience books 13:00 Mathematics resources & pitfalls 15:40 Looking of project ideas 18:59 Finding data to practice with 19:29 Final advise
My name is Artem, I'm a computational neuroscience student and researcher.
In this video we will talk about the fundamental role of lognormal distribution in neuroscience. First, we will derive it through Central Limit Theorem, and then explore how it support brain operations on many scales - from cells to perception.
Special thanks to Crimson Ghoul for providing English subtitles!
REFERENCES:
1.Buzsáki, G. & Mizuseki, K. The log-dynamic brain: how skewed distributions affect network operations. Nat Rev Neurosci 15, 264–278 (2014). 2.Ikegaya, Y. et al. Interpyramid Spike Transmission Stabilizes the Sparseness of Recurrent Network Activity. Cerebral Cortex 23, 293–304 (2013). 3.Loewenstein, Y., Kuras, A. & Rumpel, S. Multiplicative Dynamics Underlie the Emergence of the Log-Normal Distribution of Spine Sizes in the Neocortex In Vivo. Journal of Neuroscience 31, 9481–9488 (2011). 4.Morales-Gregorio, A., van Meegen, A. & van Albada, S. J. Ubiquitous lognormal distribution of neuron densities across mammalian cerebral cortex. http://biorxiv.org/lookup/doi/10.1101/2022.03.17.480842 (2022) doi:10.1101/2022.03.17.480842.
OUTLINE: 00:00 Introduction 01:15 What is Normal distribution 03:03 Central Limit Theorem 04:23 Normality in biology 05:46 Derivation of lognormal distribution 10:20 Division of labour in the brain 12:20 Generalizer and specialist neurons 13:37 How lognormality arises 15:19 Conclusion 16:00 Shortform: sponsor message 16:54 Outro
CREDITS: Icons by biorender.com Mathematical animations were created using Manim CE python library - https://www.manim.community/How to overcome study procrastination | 3 powerful tipsArtem Kirsanov2022-04-19 | Shortform link: shortform.com/artem
In this video I'll talk about 3 amazing tips, which help you to overcome study procrastination.
My name is Artem, I'm a computational neuroscience student and researcher. Socials: Twitter: twitter.com/ArtemKRSV
OUTLINE: 00:00 Introduction 00:53 Shortform message 01:55 Why we procrastinate 03:10 Yin Yang technique 06:10 Time-based mindset 08:05 Spacing out 10:38 Overview & conclusion
In this video we will explore a very interesting paper published in Nature in 2022, which describes the hidden torus in the neuronal activity of cells in the entorhinal cortex, known as grid cells.
My name is Artem, I'm a computational neuroscience student and researcher. Socials: Twitter: twitter.com/ArtemKRSV Special thanks to Crimson Ghoul for providing English subtitles!
REFERENCES: 1.Gardner, R. J. et al. Toroidal topology of population activity in grid cells. Nature 602, 123–128 (2022). 2.Pisokas, I., Heinze, S. & Webb, B. The head direction circuit of two insect species. eLife 9, e53985 (2020). 3.Shilnikov, A. L. & Maurer, A. P. The Art of Grid Fields: Geometry of Neuronal Time. Front. Neural Circuits 10, (2016). 4.Moser, M.-B., Rowland, D. C. & Moser, E. I. Place Cells, Grid Cells, and Memory. Cold Spring Harb Perspect Biol 7, a021808 (2015). 5.Lewis, M., Purdy, S., Ahmad, S. & Hawkins, J. Locations in the Neocortex: A Theory of Sensorimotor Object Recognition Using Cortical Grid Cells. http://biorxiv.org/lookup/doi/10.1101/436352 (2018) doi:10.1101/436352.
OUTLINE: 00:00 Introduction 00:48 Shortform message 01:41 Disclaimer 02:32 Grid cells & modules 04:48 How data was acquired 05:38 Toroidal coordinates 06:40 Invariance of the torus 07:43 Continuous attractor network 08:10 Outro
CREDITS: Footage of mouse running on a torus: Helmet and the Norwegian University of Science and Technology's Kavli Institute for Systems Neuroscience eurekalert.org/multimedia/814124
Thumbnail illustration: by Helmet and the Kavli Institute for Systems Neuroscience
Some video footage taken from: "Edvard and May-Britt Moser: A journey into entorhinal cortex" by NTNU University ( youtube.com/watch?v=jYCR0pQLd_U )
In this video I talk about my effective note-taking workflow I use in my university. We will talk about why "conventional" notes are ineffective and how instead you can combine Zettelkasten and flashcard note-taking techniques to achieve peak academic performance and gain deep understanding
My name is Artem, I'm a computational neuroscience student and researcher at Moscow State University. Socials: Twitter: twitter.com/ArtemKRSV
Study about intentional forgetting: Eskritt M, Ma S. Intentional forgetting: note-taking as a naturalistic example. Mem Cognit. 2014 Feb;42(2):237-46. doi: 10.3758/s13421-013-0362-1. PMID: 24014168.
How do you remember everything you read? In this video I talk about how you can use a spaced repetition platform Anki (apps.ankiweb.net) to remember what you read, by turning books and papers into flashcards
My name is Artem, I'm a computational neuroscience student and researcher at Moscow State University. Socials: Twitter: twitter.com/ArtemKRSV
OUTLINE 00:00 - Introduction 01:21 - Memory decay & how to fight it 03:23 - Why memorise at all? - Bloom's taxonomy 04:42 - Shortform message 05:51 - Creating effective flashcards 09:01 - Conclusion & outro
B-roll footage by pexels.comMemory Consolidation: Time Machine of the BrainArtem Kirsanov2022-02-16 | Visit brilliant.org/ArtemKirsanov to get started learning STEM for free, and the first 200 people will get 20% off their annual premium subscription.
How does sleep help us remember things better? In this video we will explore the wonders of the hippocampus - a brain region involved in memory consolidation and spatial navigation. We will talk about the phenomenon of hippocampal replay and the Sharp Wave Ripples.
My name is Artem, I'm a computational neuroscience student and researcher at Moscow State University. Socials: Twitter: twitter.com/ArtemKRSV Special thanks to Crimson Ghoul for providing English subtitles!
OUTLINE: 00:00 Introduction 01:01 Sponsor message 02:23 Cortex and Hippocampus 04:52 Sharp Wave Ripple 06:45 Fast-reverse memory replay 08:55 Fast-forward replay 10:03 Conclusion and Outlook
REFERENCES: 1.Ólafsdóttir, H. F., Bush, D. & Barry, C. The Role of Hippocampal Replay in Memory and Planning. Current Biology 28, R37–R50 (2018). 2.Hannah R. Joo & Loren M. Frank. The hippocampal sharp wave-ripple in memory retrieval for immediate use and consolidation. Nature Reviews Neuroscience (2018) doi:10.1038/s41583-018-0077-1. 3.Buzsáki, G. The brain from inside out. (Oxford University Press, 2019). 4.Roux, L., Hu, B., Eichler, R., Stark, E. & Buzsáki, G. Sharp wave ripples during learning stabilize the hippocampal spatial map. Nat Neurosci 20, 845–853 (2017). 5.Girardeau, G., Benchenane, K., Wiener, S. I., Buzsáki, G. & Zugaro, M. B. Selective suppression of hippocampal ripples impairs spatial memory. Nat Neurosci 12, 1222–1223 (2009). 6.Feld, G. B. & Born, J. Sculpting memory during sleep: concurrent consolidation and forgetting. Current Opinion in Neurobiology 44, 20–27 (2017). 7.Ambrose, R. E., Pfeiffer, B. E. & Foster, D. J. Reverse Replay of Hippocampal Place Cells Is Uniquely Modulated by Changing Reward. Neuron 91, 1124–1136 (2016). 8.Marcelo G. Mattar & Nathaniel D. Daw. Prioritized memory access explains planning and hippocampal replay. Nature Neuroscience (2018) doi:10.1038/s41593-018-0232-z. 9.Jens G. Klinzing, Niels Niethard, & Jan Born. Mechanisms of systems memory consolidation during sleep. Nature Neuroscience (2019) doi:10.1038/s41593-019-0467-3. 10.Penelope A. Lewis & Daniel Bendor. How Targeted Memory Reactivation Promotes the Selective Strengthening of Memories in Sleep. Current Biology (2019) doi:10.1016/j.cub.2019.08.019. 11.Wenbo Tang, Justin D. Shin, Loren M. Frank, & Shantanu P. Jadhav. Hippocampal-Prefrontal Reactivation during Learning Is Stronger in Awake Compared with Sleep States. The Journal of Neuroscience (2017) doi:10.1523/jneurosci.2291-17.2017. 12.Buzsáki, G. Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning. Hippocampus 25, 1073–1188 (2015). 13.Diba, K. & Buzsáki, G. Forward and reverse hippocampal place-cell sequences during ripples. Nat Neurosci 10, 1241–1242 (2007). 14.Shin, J. D., Tang, W. & Jadhav, S. P. Dynamics of Awake Hippocampal-Prefrontal Replay for Spatial Learning and Memory-Guided Decision Making. Neuron 104, 1110-1125.e7 (2019). 15.Mehta, M. R. Cortico-hippocampal interaction during up-down states and memory consolidation. Nat Neurosci 10, 13–15 (2007). 16.Aussel, A., Buhry, L., Tyvaert, L. & Ranta, R. A detailed anatomical and mathematical model of the hippocampal formation for the generation of sharp-wave ripples and theta-nested gamma oscillations. J Comput Neurosci 45, 207–221 (2018).
------- B roll footage by pexels.com Icons by biorender.com ------- This video was sponsored by BrilliantZettelkasten workflow for research papers | Zotero & Obsidian linkArtem Kirsanov2022-01-31 | Shortform link: shortform.com/artem
--------- My name is Artem, I’m a computational neuroscience student and researcher. In this video I talk about how I use Zotero together with Obsidian to efficiently turn research papers into Zettelkasten notes.
Socials: Twitter: twitter.com/ArtemKRSV --------- OUTLINE: 00:00 Introduction 01:14 Zotero organization 05:03 Linking Zotero to Obsidian 06:48 Inserting a paper link 08:11 Literature notes 12:49 Final words ------- B roll footage by pexels.com Icons by flaticon.comHow to Effectively Teach Yourself ANYTHINGArtem Kirsanov2022-01-06 | Shortform link: shortform.com/artem
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My name is Artem, I’m a computational neuroscience student and researcher. In this video I talk about some fundamental principles behind effective self-studying. It is based to a large extent on the book "Ultralearning" by Scott Young. I also share with your my own observations and experience
------- B roll footage by pexels.com Icons by flaticon.comPlace cells: How your brain creates maps of abstract spacesArtem Kirsanov2021-12-02 | In this video, we will explore the positional system of the brain - hippocampal place cells. We will see how it relates to contextual memory and mapping of more abstract features
OUTLINE: 00:00 Introduction 00:53 Hippocampus 1:27 Discovery of place cells 2:56 3D navigation 3:51 Role of place cells 4:11 Virtual reality experiment 7:47 Remapping 11:17 Mapping of non-spatial dimension 13:36 Conclusion
_____________ REFERENCES:
1) Anderson, M.I., Jeffery, K.J., 2003. Heterogeneous Modulation of Place Cell Firing by Changes in Context. J. Neurosci. 23, 8827–8835. doi.org/10.1523/JNEUROSCI.23-26-08827.2003
2) Aronov, D., Nevers, R., Tank, D.W., 2017. Mapping of a non-spatial dimension by the hippocampal–entorhinal circuit. Nature 543, 719–722. doi.org/10.1038/nature21692
3) Bostock, E., Muller, R.U., Kubie, J.L., 1991. Experience-dependent modifications of hippocampal place cell firing. Hippocampus 1, 193–205. doi.org/10.1002/hipo.450010207
4) Christopher D. Harvey, Forrest Collman, Daniel A. Dombeck, David W. Tank, 2009. Intracellular dynamics of hippocampal place cells during virtual navigation. Nature. doi.org/10.1038/nature08499
5) Grieves, R.M., Jedidi-Ayoub, S., Mishchanchuk, K., Liu, A., Renaudineau, S., Jeffery, K.J., 2020. The place-cell representation of volumetric space in rats. Nat Commun 11, 789. doi.org/10.1038/s41467-020-14611-7
6) Jeffery, K.J., 2011. Place Cells, Grid Cells, Attractors, and Remapping. Neural Plasticity 2011, 1–11. doi.org/10.1155/2011/182602
7) Latuske, P., Kornienko, O., Kohler, L., Allen, K., 2018. Hippocampal Remapping and Its Entorhinal Origin. Front. Behav. Neurosci. 11, 253. doi.org/10.3389/fnbeh.2017.00253
8) Laura Lee Colgin, Edvard I. Moser, May-Britt Moser, 2008. Understanding memory through hippocampal remapping. Trends in Neurosciences. doi.org/10.1016/j.tins.2008.06.008
10) Moita, M.A.P., 2004. Putting Fear in Its Place: Remapping of Hippocampal Place Cells during Fear Conditioning. Journal of Neuroscience 24, 7015–7023. doi.org/10.1523/JNEUROSCI.5492-03.2004
11) O’Keefe, J., Burgess, N., 1996. Geometric determinants of the place fields of hippocampal neurons. Nature 381, 425–428. doi.org/10.1038/381425a0
12) Robinson, N.T.M., Descamps, L.A.L., Russell, L.E., Buchholz, M.O., Bicknell, B.A., Antonov, G.K., Lau, J.Y.N., Nutbrown, R., Schmidt-Hieber, C., Häusser, M., 2020. Targeted Activation of Hippocampal Place Cells Drives Memory-Guided Spatial Behavior. Cell 183, 1586-1599.e10. doi.org/10.1016/j.cell.2020.09.061
13) Wohlgemuth, M.J., Yu, C., Moss, C.F., 2018. 3D Hippocampal Place Field Dynamics in Free-Flying Echolocating Bats. Front. Cell. Neurosci. 12, 270. doi.org/10.3389/fncel.2018.00270
_______ Socials: VK: vk.com/atpsynthaseMy simple note-taking setup | Zettelkasten in Obsidian | Step-by-step guideArtem Kirsanov2021-11-03 | In this video I show you my simple yet powerful setup in Obsidian for taking Zettelkasten notes (which plugins I recommend using, my tagging system, etc) I also show a step-by-step guide how to set it up yourself
OUTLINE: 00:00 Introduction 2:14 How Obsidian works and main definitions 4:00 My 4-folder organisation 5:13 How to set everything up from scratch 7:55 Sliding Panes & Admonition plugins 8:28 Templates 10:08 Creating notes 12:25 Tagging & classifying notes 13:16 - Maps of content (MOCs) 14:40 - Final words 16:07 - Outro
--------- B-roll footage by: pexels.com Music by: pixabay.com Iconds by: flaticon.comHow to read papers effectively | Research reading techniqueArtem Kirsanov2021-10-09 | In this video I talk about how to approach reading
a research article, how papers are structured,
in what order to read and how to highlight them
OUTLINE: 00:00 Introduction 1:06 Wrong approach 1:41 Have a clear goal 3:37 Inner filter 4:54 Structure of a paper 6:31 In what order to read 8:56 Effective highlighting 10:31 Further processing 10:59 Outro
--------- B-roll footage by pexels.comMind mapping tutorial for students | Tips & SoftwareArtem Kirsanov2021-09-18 | In this video I talk about what are mind maps, why they work and how to properly create one.
OUTLINE: 0:00 Introduction 0:47 Why mind maps work 2:49 What is a mind map? 3:09 Rules for creating 3:15 1) Hierarchical 3:34 2) Colorful 3:50 3) Use of images 4:43 4) Brief and valuable 5:39 5) Less than five divisions 6:05 My Ipad workflow 7:36 Outro
Socials: VK: vk.com/atpsynthaseHow to choose a note-taking app | Zettelkasten | Notion vs Roam vs ObsidianArtem Kirsanov2021-09-10 | In this video I discuss most popular applications for Zettelkasten note-taking, what I recommend to get started and how to approach any new application that might come out in the future
OUTLINE: 0:00 Introduction 1:55 Mindset for note-taking software 5:51 Notion 7:55 Roam research 8:15 Remnote 8:45 Obsidian 9:49 Take home message
Socials: VK: vk.com/atpsynthaseHow to focus when studying from homeArtem Kirsanov2021-07-27 | In this video I'll tell you practical tips that I use on a daily basis to stay focused during my study sessions.
OUTLINE: 0:00 - Introduction 1:00 - Part 1: Things to do before 1:20 - Bring back the spark 2:03 - Gradually increase the focus 3:58 - Dedicate a time frame 4:36 - Productive environment 5:45 - Behavioural and environmental hooks 8:08 - Part 2: Maintain the focus 9:36 - Eliminate distractions: Phone 11:06 - Eliminate distractions: Computer 15:32 - Part 3: After the session 16:06 - Short breaks 16:51 - Big block of active rest 17:39 - Flowchart & conclusions
Socials: VK: vk.com/atpsynthase Email: ArtemKirsanov2606@gmail.comYour brain CANT Multitask - Heres whyArtem Kirsanov2021-07-16 | This video explores what attention really is, what role it plays in learning and why people can't multitask - the issue of attention residue
OUTLINE: 0:00 - Sneak peek 0:20 - Introduction 0:57 - Why we need attention 1:46 - Thalamus as attentional filter 3:06 - Higher attentional systems 3:40 - Role of attention in learning 4:42 - Attention residue 6:00 - Conclusions and references
Socials: VK: vk.com/atpsynthaseInterleaving vs Spaced repetition | Study hacksArtem Kirsanov2021-07-02 | In this video I'll tell you about interleaving - a very easy and simple trick, which can immensely boost your learning of any subject
OUTLINE: 0:00 - Introduction 0:55 - What is interleaving? 2:02 - Spaced repetition vs Interleaving 3:04 - Strategy choice 5:06 - Difference detection and abstraction 6:35 - How to implement 7:46 - Conclusion and references
Socials: VK: vk.com/atpsynthaseUnderstanding note-taking | ZettelkastenArtem Kirsanov2021-06-23 | In this video I'll talk about a Zettelkasten note-taking system, invented by Niklas Luhmann and why it works, so that you can take inspiration from these principles and incorporate them into your own workflow.
Socials: VK: vk.com/atpsynthaseA tool to discover research papers - Research RabbitArtem Kirsanov2021-06-12 | Referral code: WKELe4G
In this video I'll talk about Research Rabbit - a new tool (still in beta testing) which uses AI to help you discover relevant and interesting research articles, tailored specifically to your interests
OUTLINE: 0:00 - Introduction 1:20 - Requesting an early access 2:25 - Move to the top of the list 2:53 - Two different websites 4:18 - Using the website: create a collection 5:17 - Different sections 7:36 - Graph visualization 8:50 - Return to the starting point 9:17 - Workflow conclusion 10:00 - Individualised emails 10:08 - Outro & References
Socials: VK: vk.com/atpsynthaseBiological Clock - Breakthrough junior challenge 2018Artem Kirsanov2018-07-01 | This is my #breakthroughjuniorchallenge video about Biological Clock
P.S. Physiology or Medicine Nobel Prize 2017 was awarded for research in this topic c: